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R/convertFromJafroc.R
In tpfp: Counts the Number of True Positives and False Positives
Defines functions
convertFromJafroc
convertFromJafrocCasewise
Documented in
convertFromJafroc
convertFromJafrocCasewise
#' @title Convert \code{.xlsx} File
#' of \strong{\emph{Jafroc}}
#' into \R object
#'
#'@description
#'\strong{\emph{Convert an FROC dataset}}
#'\describe{
#'\item{ \strong{\emph{from}} }{ \code{.xlsx} file of \strong{\emph{Jafroc}} }
#'\item{ \strong{\emph{into}} }{ \R object }
#'}
#'
#'
#'@details
# Create a dataset to be passed into the
# function \code{ \link{fit_Bayesian_FROC}}.
#'Convert an Excel file whose extension
#'is \code{.xlsx} of Jafroc format to
#' an \R object representing FROC data.
# to which we will apply functions in
# this package such as \code{\link{fit_Bayesian_FROC}()}.
#'
#'@param No.of.Modalities A positive integer, indicating the number of \emph{modalities} for FROC data-set in \code{.xlsx} file.
#'@param No.of.readers A positive integer, indicating the number of \emph{readers} for FROC data-set in an \code{.xlsx} file.
#'@param No.of.confidence.levels A positive integer, indicating the number of \emph{confidence levels} for FROC data-set in \code{.xlsx} file.
#'@param debug A logical, whether, inner objects in function is push out into global environment or not.
#'@references Bayesian Models for Free-response
#' Receiver Operating Characteristic Analysis,pre-print
#'@return A list, representing FROC data.
#'@details
#' The return values include a
#' list which can be passed to the function \code{fit_Bayesian_FROC}.
#' For data of Jafroc, running this function,
#' we immediately can fit the author's Bayesian
#' FROC model to this return value.
#'
#' The Jafroc software's format consists of
#' suspicious locations marked by readers and true locations.
#' Such data is \emph{redundant} for our
#' Bayesian statistical models.
#' So, we reduce the information of data to the number of
#' false positives and number of hits for
#' each confidence levels by this function.
#'
#'
#'
#' Data can be calculated from the following
#' Jafroc data, in which there are more information than TP and FP.
#' In fact, in the Jafroc data, the FP and TP are counted for each images, each lesions etc.
#' So, it has more information.
#'
#'
#' It causes limitation of our model.
#' So, our model start to fit a model to
#' the reduced data from Jafroc. So,
#' the redunction will cause the non accuracy
#' evaluation of the observer performance.
#' The future research I should start the
#' Jafroc formulation.
#'
#'
#'
#'@format The \code{.xlsx} file of Jafroc
#'consists of three sheets named \strong{\emph{TP, FP, Truth}}, \strong{\emph{ precisely! Correctly! }}
#'(other names never be permitted !!)
#'
#'
#'\strong{\emph{----------------------------------- TP ------------------------------------------}}
#'
#'
#'A sheet named \strong{\emph{TP}} consists of five columns \strong{\emph{ precisely }} named from the right hand side:
#'
#'\strong{\emph{ReaderID, ModalityID, CaseID, LesionID, TP_Rating.}}
#'
#'
#'\strong{\emph{NOTE.}}
#'\describe{
#'\item{ \strong{\emph{CaseID}} }{ Note that the above word \strong{\emph{CaseID}} means the Image ID vectors indicating the ID of radiographs. That is "case = image = radiograph".}
#'\item{ \strong{\emph{the first row}} }{ Note that the first row of each sheat of \code{.xlsx} file is constructed by the names of column as follows: }
#'}
#'
#'\strong{An Example of the sheet named \emph{TP} in a \emph{\code{.xlsx}} file for the \emph{Jafroc} software}
#'
#'
#'\strong{ Interpretation of table}
#'
#' Throughout this explanation, we follow the convention that readers are male.
#'
#' For example,
#' the first row means
#' the first reader (ReaderID=1) correctly find
#' the first lesion (LesionID = 1)
#' in the first image (CaseID = 1)
#' taken by the first modality (ModalityID = 1)
#' with his rating 5 (TP_Rating =5).
#'
#' Similarly,
#' the second row means
#' the first reader (ReaderID=1) correctly find
#' the 4-th lesion (LesionID = 4)
#' in the second image (CaseID = 2)
#' taken by the 2-nd modality (ModalityID = 2)
#' with his rating 4 (TP_Rating = 4).
#'
#'
#'
#' \tabular{ccccc}{
#' ReaderID \tab ModalityID \tab CaseID \tab LesionID \tab TP_Rating.\cr
#' -------------------\tab-------------------\tab-------------------\tab-------------------\tab------------------\cr
#' 1 \tab 1 \tab 1 \tab 1\tab 5\cr
#' 1 \tab 2 \tab 2 \tab 1\tab 4\cr
#' 1 \tab 3 \tab 4 \tab 1\tab 5\cr
#' 1 \tab 1 \tab 8 \tab 1\tab 3\cr
#' 1 \tab 2 \tab 8 \tab 2\tab 4\cr
#' 1 \tab 3 \tab 9 \tab 1\tab 4\cr
#' 1 \tab 1 \tab 9 \tab 2\tab 3\cr
#' 1 \tab 2 \tab 9 \tab 3\tab 5\cr
#' 1 \tab 3 \tab 11 \tab 1\tab 3\cr
#' 2 \tab 1 \tab 1 \tab 1\tab 4\cr
#' 2 \tab 2 \tab 4 \tab 1\tab 4\cr
#' 2 \tab 3 \tab 5 \tab 1\tab 4\cr
#' 2 \tab 1 \tab 8 \tab 1\tab 1\cr
#' 2 \tab 2 \tab 8 \tab 2\tab 2\cr
#' 2 \tab 3 \tab 8 \tab 3\tab 2\cr
#' 2 \tab 1 \tab 10 \tab 1\tab 3\cr
#' 2 \tab 2 \tab 10 \tab 2\tab 2\cr
#' 2 \tab 3 \tab 11 \tab 1\tab 2\cr
#'
#' : \tab : \tab : \tab :\tab :\cr
#' : \tab : \tab : \tab :\tab :\cr }
#'
#'
#'\strong{\emph{----------------------------------- FP ------------------------------------------}}
#'
#'
#'
#' A sheet named \strong{FP} consists of four columns \strong{\emph{ precisely }}
#' named from the right hand side: \strong{ReaderID, ModalityID, CaseID, FP_Rating}
#'\strong{An Example of a sheet named FP in a \code{.xlsx} file for the Jafroc software}
#'
#'
#'
#'
#'
#'
#'\strong{ Interpretation of table}
#'
#'
#' For example,
#' the first row means
#' the first reader (ReaderID=1) makes
#' a false alarm location
#' in the first image (CaseID = 1)
#' taken by the first modality (ModalityID = 1)
#' with his rating 2 (TP_Rating =2).
#'
#' Similarly,
#' the second row means
#' the first reader (ReaderID=1) makes
#' a false alarm location
#' in the second image (CaseID = 2)
#' taken by the 2-nd modality (ModalityID = 2)
#' with his rating 1 (TP_Rating = 1).
#'
#'
#' Similarly,
#' the 6-th and 7-th rows mean that
#' the first reader (ReaderID=1) makes
#' two false alarm location
#' in the second patient (CaseID = 2).
#' The first false alarm is in the image
#' taken by the 1-st modality (ModalityID = 1)
#' with his rating 1 (TP_Rating = 1).
#' The second false alarm is in the image
#' taken by the 3-rd modality (ModalityID = 3)
#' with his rating 2 (TP_Rating = 2).
#'
#'
#'
#'
#'
#'
#' \tabular{cccc}{
#' ReaderID \tab ModalityID \tab CaseID \tab FP_Rating.\cr
#' -------------------\tab-------------------\tab-------------------\tab------------------\cr
#' 1 \tab 1 \tab 1 \tab 2\cr
#' 1 \tab 2 \tab 2 \tab 1\cr
#' 1 \tab 3 \tab 3 \tab 1\cr
#' 1 \tab 1 \tab 5 \tab 2\cr
#' 1 \tab 2 \tab 7 \tab 1\cr
#' 1 \tab 3 \tab 7 \tab 2\cr
#' 1 \tab 1 \tab 9 \tab 3\cr
#' 1 \tab 2 \tab 9 \tab 4\cr
#' 1 \tab 3 \tab 10 \tab 1\cr
#' 2 \tab 1 \tab 1 \tab 2\cr
#' 2 \tab 2 \tab 2 \tab 3\cr
#' 2 \tab 3 \tab 3 \tab 4\cr
#' 2 \tab 1 \tab 8 \tab 1\cr
#' 2 \tab 2 \tab 9 \tab 1\cr
#' 2 \tab 3 \tab 11 \tab 1\cr
#' 2 \tab 1 \tab 14 \tab 1\cr
#' 2 \tab 2 \tab 15 \tab 1\cr
#' 2 \tab 3 \tab 21 \tab 2\cr
#' : \tab : \tab :\tab :\cr
#' : \tab : \tab :\tab :\cr
#' }
#'
#'
#'
#'
#'\strong{\emph{----------------------------------- Truth ------------------------------------------}}
#'
#'A sheet named \strong{Truth } consists of three columns
#' \strong{\emph{ precisely }} named from the right
#' hand side:\strong{CaseID, LesionID, Weight} .
#'
#'\strong{An Example of a sheet named Truth in a \code{.xlsx} file for the Jafroc software}
#'
#'
#'\strong{ Interpretation of table}
#'
#'For example, the first image (CaseID = 1) contains
#'three lesions each of which is named 1,2,3, namely LesionID = 1,2,3.
#'For example, the second image (CaseID = 2) contains
#'two lesions each of which is named 1,2, namely LesionID = 1,2.
#'For example, the third image (CaseID = 3) contains
#'a sinle lesion named 1, namely LesionID = 1.
#'
#' \tabular{ccc}{
#' CaseID \tab LesionID \tab Weight\cr
#' -------------------\tab-------------------\tab------------------\cr
#' 1 \tab 1 \tab 0.3333...\cr
#' 1 \tab 2 \tab 0.3333...\cr
#' 1 \tab 3 \tab 0.3333...\cr
#' 2 \tab 1 \tab 0.5\cr
#' 2 \tab 2 \tab 0.5\cr
#' 3 \tab 1 \tab 1\cr
#' 4 \tab 1 \tab 0.25\cr
#' 4 \tab 2 \tab 0.25\cr
#' 4 \tab 3 \tab 0.25\cr
#' 4 \tab 4 \tab 0.25\cr
#' 5 \tab 1 \tab 0.5\cr
#' 5 \tab 2 \tab 0.5\cr
#' 6 \tab 1 \tab 0.3333...\cr
#' 6 \tab 2 \tab 0.3333...\cr
#' 6 \tab 3 \tab 0.3333...\cr
#' 7 \tab 1 \tab 0.3333...\cr
#' 7 \tab 2 \tab 0.3333...\cr
#' 7 \tab 3 \tab 0.3333...\cr
#' 8 \tab 1 \tab 0.25\cr
#' 8 \tab 2 \tab 0.25\cr
#' 8 \tab 3 \tab 0.25\cr
#' 8 \tab 4 \tab 0.25\cr
#' : \tab :\tab :\cr
#' : \tab :\tab :\cr
#' }
#'
#'
#'
#'
#' Note that the weght are used
#' such that each image influences
#' a same effect on the esimates.
#' Without weight, the images including
#' many targets (lesions) will have very strong effect
#' on the estimates.
#' To avoid such bias, Jafroc uses weight.
#' In another context,
#' weight would be used to specify more important lesions in each image.
#'
#' In this package, we do not use the information of weight.
#'Since the theory of the author of this package did not consider such weight.
#' In the future I have to include the notion of weight.
#'Jafroc use the notion fo figure of metric as non parametric manner.
#'So, it seems difficult to include it in the Bayesian model,
#'since generally speaking, Bayesian methodology is parametric.
#'
#'@import readxl
#'
#'@seealso
#'Rjafroc, which is unfortunately not on CRAN, now 2019 Jun 19.
#' Or JAFROC software in the Chakarboty's Web page.
#' Unfortunately, this software is no longer supported.
#'
#'@examples
#'
# \dontrun{
#'## Only run examples in interactive R sessions
#'if (interactive()) {
# ####1#### ####2#### ####3#### ####4#### ####5#### ####6#### ####7#### ####8#### ####9####
#'#========================================================================================
#'# Example for convert the Jafroc data
#'#========================================================================================
#'
#' # Work Flow
#'
#' # step 0) Make an .xlsx file "JafrocDatasetExample.xlsx" as example data
#' # to be passed to the function by interactive manner.
#' # step 1) Convert the "JafrocDatasetExample.xlsx" file to an R object
#'
#'
#'
#'
#'
#'
#'
#'
#'
#'
#'
#'
#'
#' # In the following code, when dialog box asks you to select a file, then
#' # Select "JafrocDatasetExample.xlsx" in Working directory.
#'
#'
#' # This is important, so I write twice, again.
#'
#'
#' # In the following code, when dialog box asks you to select a file, then
#' # Select "JafrocDatasetExample.xlsx" in Working directory.
#'
#'
#'
#' # In the following code, when dialog box asks you to select a file, then
#' # Select "JafrocDatasetExample.xlsx" in Working directory.
#'
#'
#'
#'
#'
#'#========================================================================================
#'# step 0) Make a Jafroc data as an example dataset
#'#========================================================================================
#'
#'# If you can find the xlsx file named JAFROC_data.xlsx
#'# in the director "inst/extdata" of this package,
#'# Then this step 0) is redundant. The author prepares this example for the people who
#'# cannot find the xlsx file in the "inst/extdata" of this package.
#' # Using an xlsx file ( which is named JAFROC_data.xlsx and
#' # included in the director "inst/extdata" of this package,)
#' # we can reconstruct it as follows:(If someone can obtain the Excel file
#' # from the path tpfp/inst/extdata/JAFROC_data.xlsx, then the following code
#' # is not required to run, because the same xlsx file is created.
#'
#'
#'
#'
#' Truth <- readxl::read_excel(system.file("extdata",
#' "JAFROC_data.xlsx",
#' package="tpfp"),
#' sheet = "Truth")
#'##### utils::View(Truth)
#'
#' TP <- readxl::read_excel( system.file("extdata",
#' "JAFROC_data.xlsx",
#' package="tpfp"),
#' sheet = "TP")
#'#### utils::View(TP)
#'
#' FP <- readxl::read_excel( system.file("extdata",
#' "JAFROC_data.xlsx",
#' package="tpfp"),
#' sheet = "FP")
#'#### utils::View(FP)
#'
#'
#'
#'
#'
#'sample <- list(TP=TP,FP=FP,Truth=Truth)
#'openxlsx::write.xlsx(sample,"JafrocDatasetExample.xlsx")
#'
#' tcltk::tkmessageBox(
#' message="A file named
#'
#' JafrocDatasetExample.xlsx
#'
#'
#' is created in the working directory")
#'
#'
#'# Now, we obtain an excel file named "JafrocDatasetExample.xlsx", which is same as
#'# the JAFROC_data.xlsx.
#'# whose format is available in the Jafroc software developed by Chakraborty.
#'# Note that if you have an excel file
#'# which is formulated correctolly for our package,
#'# then the above process does not be required.
#'
#'
#'
#'
#'# (0) From the above, we obtain "JafrocDatasetExample.xlsx"
#'# which is the multiple reader and multiple modality dataset
#'# for Jafroc analysis which is Chakraborty's software Jafroc or the R package Rjafroc
#'# but NOT implemented in our package,.
#'
#'
#'
#'
#'#========================================================================================
#'# step 1) Convert the Jafroc data obtained in the above
#'#========================================================================================
#'
#'# (1) Using "JafrocDatasetExample.xlsx" as an example excel file,
#'# we run the function to convert the excel file from Jafroc format
#'# to our format:
#'
#'
#' dataList <- convertFromJafrocCasewise(
#' No.of.Modalities =5,
#' No.of.readers =4,
#' No.of.confidence.levels = 5
#' )
#'
#'
#'
#'# In the variable, there is no xlsx files, since it is selected by interactive manner.
#'# So, please select the xlsx file, i.e., "JafrocDatasetExample.xlsx" obtained
#'# in step 0) or your own Jafroc .xlsx file.
#'
#'
#'}### Only run examples in interactive R sessions
#'
#'
# }#' \dontrun{
#'
#'
#' @export
convertFromJafrocCasewise <- function(
No.of.Modalities,
No.of.readers,
No.of.confidence.levels,
# No.of.images
debug = FALSE
){
M <- No.of.Modalities
Q <- No.of.readers
C <- No.of.confidence.levels
# NI <- No.of.images
########### PASS ########## START
message("* Please select a xlsx file to calculate its number of false alarms and true positives for each confidenve level.")
message("* The lowest confidence level should be 1, not zero.")
tcltk::tkmessageBox(message="
* Select a Jafroc file whose extesion is .xlsx to be converted into an R object.
* The return.value is used to calculate FROC bayesian model in this package by running the following code:
fit_Bayesian_FROC(return.value)")
path<-tcltk::tclvalue(tcltk::tkgetOpenFile())
if (!nchar(path)){
tcltk::tkmessageBox(
message="* No file was selected! \n* Exit. \n\n\n* I love you! \n\n\n* You are not alone,... \n perhaps,...\n piggy is with you \n or doggy and doggy are with you,... ")
return(message("Suspend a process."))
}else{
xlsxfilename <- basename(path)
tcltk::tkmessageBox(message=paste("* The selected file name:",xlsxfilename,".
* More precisely, its path is the follwing:\n", path, "."))
}
# path
xlsxfilename <- basename(path)
directoryname <- dirname(path)
########### PASS ########## Fin
message("\n \n \n \n Wait...")
fileTP<-xlsx::read.xlsx(path, sheetIndex ="TP")
message("\n Wait...")
fileFP<-xlsx::read.xlsx(path, sheetIndex ="FP")
message("\n Wait... \n\n\n")
fileTruth<-xlsx::read.xlsx(path, sheetIndex ="Truth")
if(min(fileTruth$CaseID) ==0) NI <- max(fileTruth$CaseID) +1
if(min(fileTruth$CaseID) ==1) NI <- max(fileTruth$CaseID) +0
if(min(fileTruth$CaseID) ==2) NI <- max(fileTruth$CaseID) -1
if(min(fileTruth$CaseID) ==3) NI <- max(fileTruth$CaseID) -2
minimal_CaseID <- min(fileTruth$CaseID)
maximal_CaseID <- max(fileTruth$CaseID)
# caseID_deseased -----------
for (iii in 1:nrow(fileTruth)) {
fileTruth_deseased_only <- fileTruth[ !(fileTruth$LesionID == 0),]
# caseIDs_deseased <- fileTruth[ !(fileTruth$LesionID == 0),]
}
caseIDs_deseased <- unique(fileTruth_deseased_only$CaseID)
# number_of_caseIDs_deseased
# NL_casewise----
NL_casewise <-array(0,c(NI))
for (caseid in minimal_CaseID:maximal_CaseID) {
NL_casewise[caseid] <- max(fileTruth[fileTruth$CaseID==caseid,]$LesionID)
}
if(debug){
my_global_env <- globalenv()
my_global_env$NL_casewise <- NL_casewise
print.data.frame( data.frame( NL_casewise= NL_casewise))
}
# head(fileTP )
# head(fileFP )
# head(fileTruth)
# names(fileTP)
# names(fileFP )
# names(fileTruth)
#
# C <- 5
# M<-5
# Q<-4
# M <-max(max(fileTP$ModalityID),max( fileFP$ModalityID))
# M <-as.integer (max( max( fileFP$ModalityID)))
# Q <-as.integer (max( max( fileTP$ReaderID )))
if( min( c( fileTP$CaseID, fileTP$CaseID, fileTruth$CaseID) )==0 ) NI <- max(c( fileTP$CaseID, fileTP$CaseID, fileTruth$CaseID) ) +1
if( min( c( fileTP$CaseID, fileTP$CaseID, fileTruth$CaseID) )==1 ) NI <- max(c( fileTP$CaseID, fileTP$CaseID, fileTruth$CaseID) )
SRSC <-array(0,c(NI,M,Q,C))
harray <-array(0,c(NI,M,Q,C))
farray <-array(0,c(NI,M,Q,C))
# if( min( c( fileTP$CaseID, fileTP$CaseID, fileTruth$CaseID) )==1 ){
for (iii in 1:nrow(fileTP)) {
harray[ fileTP$CaseID[iii] , fileTP$ModalityID[iii] , fileTP$ReaderID[iii] ,fileTP$TP_Rating[iii] ] <- 1+ harray[ fileTP$CaseID[iii] , fileTP$ModalityID[iii] , fileTP$ReaderID[iii] ,fileTP$TP_Rating[iii] ]
}
for (iii in 1:nrow(fileFP)) {
farray[ fileFP$CaseID[iii] , fileFP$ModalityID[iii] , fileFP$ReaderID[iii] ,fileFP$FP_Rating[iii] ] <- 1+ farray[ fileFP$CaseID[iii] , fileFP$ModalityID[iii] , fileFP$ReaderID[iii] ,fileFP$FP_Rating[iii] ]
}
# }else if( min( c( fileTP$CaseID, fileTP$CaseID, fileTruth$CaseID) )==0 ){
# for (iii in 1:nrow(fileTP)) {
# harray[ fileTP$CaseID[iii] +1 , fileTP$ModalityID[iii] , fileTP$ReaderID[iii] ,fileTP$TP_Rating[iii] ] <- 1+ harray[ fileTP$CaseID[iii] +1 , fileTP$ModalityID[iii] , fileTP$ReaderID[iii] ,fileTP$TP_Rating[iii] ]
# }
# for (iii in 1:nrow(fileFP)) {
# farray[ fileFP$CaseID[iii] +1 , fileFP$ModalityID[iii] , fileFP$ReaderID[iii] ,fileFP$FP_Rating[iii] ] <- 1+ farray[ fileFP$CaseID[iii] +1 , fileFP$ModalityID[iii] , fileFP$ReaderID[iii] ,fileFP$FP_Rating[iii] ]
# }
#
#
#
# }
# m<- rep(1:M, each=C*Q)
# m
# q <- rep(seq(1,Q,1), M, each=C)
# q
# c <-rep(rep(C:1), M*Q)
# c
a <- caseID_m_q_c_vector_from_NI_M_Q_C(NI = NI , M = M ,Q = Q, C = C )
caseID <- a$caseID
m <- a$m
c <- a$c
q <- a$q
f <-array(0,c(NI*M*Q*C))
f
h <-array(0,c(NI*M*Q*C))
h
#
# length(m)
# length(q)
# length(c)
# length(h)
# length(f)
for(case in 1:NI)
for (mmm in 1:M) {
for (qqq in 1:Q) {
for (ccc in 1:C) {
h[ (case -1 )*M*Q*C + (mmm-1)*Q*C+(qqq-1)*C+(C+1-ccc)] <-harray[case,mmm,qqq,ccc]
f[ (case -1 )*M*Q*C + (mmm-1)*Q*C+(qqq-1)*C+(C+1-ccc)] <-farray[case,mmm,qqq,ccc]
}}}
# length(m)
# length(q)
# length(c)
# length(h)
# length(f)
yourdata <- data.frame(CaseID = caseID,
ModalityID=m,
ReaderID=q,
Confidence_lebels=c,
True_Positives=h,
False_Positives=f)
print.data.frame(yourdata,row.names = F)
if(debug){
# my_global_env <- globalenv()
my_global_env$h<-h
my_global_env$f<-f
my_global_env$harray<-harray
my_global_env$farray<-farray
my_global_env$h<-h
my_global_env$f<-f
my_global_env$M<-M
my_global_env$Q<-Q
my_global_env$C<-C
my_global_env$m<-m
my_global_env$q<-q
my_global_env$c<-c
my_global_env$n<-nrow(fileTP)
my_global_env$NI<-NI
length(h);length(f); length(q);length(m)
my_global_env$fileTP<-fileTP
my_global_env$fileFP<-fileFP
my_global_env$fileTruth<-fileTruth
my_global_env$yourdata<-yourdata
}
# yourdata
# nrow(yourdata)
# nrow(yourdata)==M*Q*C
message("\n \n wait .... \n")
xlsx::write.xlsx(yourdata, file=paste(directoryname,"/converted_",xlsxfilename,sep=""), sheetName="TPsFPs", row.names=F)
message(paste("* An excel file named converted_",xlsxfilename," is made in the directory (",directoryname,"), in which the number of false positives and true positives are calcultated for your data (",xlsxfilename ,"). \n \n ",sep = ""))
#here ----
# tcltk::tkmessageBox(
# message=paste("
# * An excel file named
#
#
# converted_",xlsxfilename,"
#
# is made in the directory
#
# ",directoryname,"
#
# in which the number of false positives and true positives are calcultated for the
#
# ",xlsxfilename ,". \n \n ",sep = "")
# )
message("
* An excel file named
converted_",xlsxfilename,"
is made in the directory
",directoryname,"
in which the number of false positives and true positives are calcultated for the
",xlsxfilename ,". \n \n ",sep = "")
#xlsx::write.xlsx(yourdata, file=paste(directoryname,"/trans... This / is important!! This is it in path !!
# data.transformed <<- read.xlsx2(paste("transformed",xlsxfilename), 1, stringsAsFactors=FALSE)
# message("2) A dataframe is made and named 'dataframe.transformed'. \n")
# message("-------------------------------------------------- \n")
NL <- nrow(fileTruth)- nrow(subset(fileTruth ,fileTruth$LesionID ==0) )
# return -----
dataList <- list(caseID=caseID,
NI=NI,
NI_deseased = length(caseIDs_deseased),
NL_casewise=NL_casewise,
caseIDs_deseased=caseIDs_deseased,
fileTruth_deseased_only=fileTruth_deseased_only,
fileTP=fileTP,
fileFP=fileFP,
fileTruth=fileTruth,
m=m,q=q,c=c,h=h,f=f, NL=NL, C=C,M=M,Q=Q,
harray=harray,farray=farray,df=yourdata)
# results <- fastDummies::dummy_cols(df, select_columns = "ModalityID")
# knitr::kable(results)
# assign("dataList", dataList, envir=globalenv())
message("* I love you 2019. Aug. 1. so,... please please me! \n\n")
message("* The retrun value is a list object which is available for model fitting by the fit_Bayesian_FROC(). \n\n")
message("* If forget to restore the return value, then use the R object [ .Last.value ] in which the disired return value is retained. \n\n")
message("* For example, execute the R script [ fit <- fit_Bayesian_FROC(.Last.value ) ] in which the disired analysis will be done. \n\n")
message("-------------------------------------------------- \n")
return(dataList)
#
# dataList.Chakra.Web$m==dataList.transformed$m
# dataList.Chakra.Web$q==dataList.transformed$q
# dataList.Chakra.Web$c==dataList.transformed$c
# dataList.Chakra.Web$h==dataList.transformed$h
# dataList.Chakra.Web$f==dataList.transformed$f
# dataList.Chakra.Web$NL==dataList.transformed$NL
# dataList.Chakra.Web$C==dataList.transformed$C
# dataList.Chakra.Web$M==dataList.transformed$M
# dataList.Chakra.Web$Q==dataList.transformed$Q
#
# m <- data.transformed$ModalityID
# q <- data.transformed$ReaderID
# c <- data.transformed$Confidence_lebels
# h <- data.transformed$True_Positives
# f <- data.transformed$False_Positives
#
#
# length(m)
# length(q)
# length(c)
# length(h)
# length(f)
# N <-M*Q*C
#
#
# m <-data$m
# q <-data$q
# c <-data$c
# h <-data$h
# f <-data$f
# NL<-data$NL
# C<-data$C
# M<-data$M
# Q<-data$Q
#
}
#' @title Convert \code{.xlsx} File
#' of \strong{\emph{Jafroc}}
#' into \R object
#'
#'@description
#'\strong{\emph{Convert an FROC dataset}}
#'\describe{
#'\item{ \strong{\emph{from}} }{ \code{.xlsx} file of \strong{\emph{Jafroc}} }
#'\item{ \strong{\emph{into}} }{ \R object }
#'}
#'
#'
#'@details
# Create a dataset to be passed into the
# function \code{ \link{fit_Bayesian_FROC}}.
#'Convert an Excel file whose extension
#'is \code{.xlsx} of Jafroc format to
#' an \R object representing FROC data
# to which we will apply functions in
# this package such as \code{\link{fit_Bayesian_FROC}()}.
#'
#'Revised 2019 Jun 19
#'Revised 2019 Dec 13
#'@param No.of.Modalities A positive integer, indicating the number of \emph{modalities} for FROC data-set in \code{.xlsx} file.
#'@param No.of.readers A positive integer, indicating the number of \emph{readers} for FROC data-set in an \code{.xlsx} file.
#'@param No.of.confidence.levels A positive integer, indicating the number of \emph{confidence levels} for FROC data-set in \code{.xlsx} file.
#'@references Bayesian Models for Free-response
#' Receiver Operating Characteristic Analysis,pre-print
#'@return A list, representing FROC data.
#'@details
#' The return values include a
#' list which can be passed to the function \code{fit_Bayesian_FROC}.
#' For data of Jafroc, running this function,
#' we immediately can fit the author's Bayesian
#' FROC model to this return value.
#'
#' The Jafroc software's format consists of
#' suspicious locations marked by readers and true locations.
#' Such data is \emph{redundant} for our
#' Bayesian statistical models.
#' So, we reduce the information of data to the number of
#' false positives and number of hits for
#' each confidence levels by this function.
#'
#'
#'
#' Data can be calculated from the following
#' Jafroc data, in which there are more information than TP and FP.
#' In fact, in the Jafroc data, the FP and TP are counted for each images, each lesions etc.
#' So, it has more information.
#'
#'
#' It causes limitation of our model.
#' So, our model start to fit a model to
#' the reduced data from Jafroc. So,
#' the redunction will cause the non accuracy
#' evaluation of the observer performance.
#' The future research I should start the
#' Jafroc formulation.
#'
#'
#'
#'@format The \code{.xlsx} file of Jafroc
#'consists of three sheets named \strong{\emph{TP, FP, Truth}}, \strong{\emph{ precisely! Correctly! }}
#'(other names never be permitted !!)
#'
#'
#'\strong{\emph{----------------------------------- TP ------------------------------------------}}
#'
#'
#'A sheet named \strong{\emph{TP}} consists of five columns \strong{\emph{ precisely }} named from the right hand side:
#'
#'\strong{\emph{ReaderID, ModalityID, CaseID, LesionID, TP_Rating.}}
#'
#'
#'\strong{\emph{NOTE.}}
#'\describe{
#'\item{ \strong{\emph{CaseID}} }{ Note that the above word \strong{\emph{CaseID}} means the Image ID vectors indicating the ID of radiographs. That is "case = image = radiograph".}
#'\item{ \strong{\emph{the first row}} }{ Note that the first row of each sheat of \code{.xlsx} file is constructed by the names of column as follows: }
#'}
#'
#'\strong{An Example of the sheet named \emph{TP} in a \emph{\code{.xlsx}} file for the \emph{Jafroc} software}
#'
#'
#'\strong{ Interpretation of table}
#'
#' Throughout this explanation, we follow the convention that readers are male.
#'
#' For example,
#' the first row means
#' the first reader (ReaderID=1) correctly find
#' the first lesion (LesionID = 1)
#' in the first image (CaseID = 1)
#' taken by the first modality (ModalityID = 1)
#' with his rating 5 (TP_Rating =5).
#'
#' Similarly,
#' the second row means
#' the first reader (ReaderID=1) correctly find
#' the 4-th lesion (LesionID = 4)
#' in the second image (CaseID = 2)
#' taken by the 2-nd modality (ModalityID = 2)
#' with his rating 4 (TP_Rating = 4).
#'
#'
#'
#' \tabular{ccccc}{
#' ReaderID \tab ModalityID \tab CaseID \tab LesionID \tab TP_Rating.\cr
#' -------------------\tab-------------------\tab-------------------\tab-------------------\tab------------------\cr
#' 1 \tab 1 \tab 1 \tab 1\tab 5\cr
#' 1 \tab 2 \tab 2 \tab 1\tab 4\cr
#' 1 \tab 3 \tab 4 \tab 1\tab 5\cr
#' 1 \tab 1 \tab 8 \tab 1\tab 3\cr
#' 1 \tab 2 \tab 8 \tab 2\tab 4\cr
#' 1 \tab 3 \tab 9 \tab 1\tab 4\cr
#' 1 \tab 1 \tab 9 \tab 2\tab 3\cr
#' 1 \tab 2 \tab 9 \tab 3\tab 5\cr
#' 1 \tab 3 \tab 11 \tab 1\tab 3\cr
#' 2 \tab 1 \tab 1 \tab 1\tab 4\cr
#' 2 \tab 2 \tab 4 \tab 1\tab 4\cr
#' 2 \tab 3 \tab 5 \tab 1\tab 4\cr
#' 2 \tab 1 \tab 8 \tab 1\tab 1\cr
#' 2 \tab 2 \tab 8 \tab 2\tab 2\cr
#' 2 \tab 3 \tab 8 \tab 3\tab 2\cr
#' 2 \tab 1 \tab 10 \tab 1\tab 3\cr
#' 2 \tab 2 \tab 10 \tab 2\tab 2\cr
#' 2 \tab 3 \tab 11 \tab 1\tab 2\cr
#'
#' : \tab : \tab : \tab :\tab :\cr
#' : \tab : \tab : \tab :\tab :\cr }
#'
#'
#'\strong{\emph{----------------------------------- FP ------------------------------------------}}
#'
#'
#'
#' A sheet named \strong{FP} consists of four columns \strong{\emph{ precisely }}
#' named from the right hand side: \strong{ReaderID, ModalityID, CaseID, FP_Rating}
#'\strong{An Example of a sheet named FP in a file of extension "\code{.xlsx}" file for the Jafroc software}
#'
#'
#'
#'
#'
#'
#'\strong{ Interpretation of table}
#'
#'
#' For example,
#' the first row means that
#' the first reader (ReaderID=1) makes
#' a false alarm location
#' in the first image (CaseID = 1)
#' taken by the first modality (ModalityID = 1)
#' with his rating 2 (TP_Rating =2).
#'
#' Similarly,
#' the second row means that
#' the first reader (ReaderID=1) makes
#' a false alarm location
#' in the second image (CaseID = 2)
#' taken by the 2-nd modality (ModalityID = 2)
#' with his rating 1 (TP_Rating = 1).
#'
#'
#' Similarly,
#' the 6-th and 7-th rows mean that
#' the first reader (ReaderID=1) makes
#' two false alarm location
#' in the second patient (CaseID = 2).
#' The first false alarm is in the image
#' taken by the 1-st modality (ModalityID = 1)
#' with his rating 1 (TP_Rating = 1).
#' The second false alarm is in the image
#' taken by the 3-rd modality (ModalityID = 3)
#' with his rating 2 (TP_Rating = 2).
#'
#'
#'
#'
#'
#'
#' \tabular{cccc}{
#' ReaderID \tab ModalityID \tab CaseID \tab FP_Rating.\cr
#' -------------------\tab-------------------\tab-------------------\tab------------------\cr
#' 1 \tab 1 \tab 1 \tab 2\cr
#' 1 \tab 2 \tab 2 \tab 1\cr
#' 1 \tab 3 \tab 3 \tab 1\cr
#' 1 \tab 1 \tab 5 \tab 2\cr
#' 1 \tab 2 \tab 7 \tab 1\cr
#' 1 \tab 3 \tab 7 \tab 2\cr
#' 1 \tab 1 \tab 9 \tab 3\cr
#' 1 \tab 2 \tab 9 \tab 4\cr
#' 1 \tab 3 \tab 10 \tab 1\cr
#' 2 \tab 1 \tab 1 \tab 2\cr
#' 2 \tab 2 \tab 2 \tab 3\cr
#' 2 \tab 3 \tab 3 \tab 4\cr
#' 2 \tab 1 \tab 8 \tab 1\cr
#' 2 \tab 2 \tab 9 \tab 1\cr
#' 2 \tab 3 \tab 11 \tab 1\cr
#' 2 \tab 1 \tab 14 \tab 1\cr
#' 2 \tab 2 \tab 15 \tab 1\cr
#' 2 \tab 3 \tab 21 \tab 2\cr
#' : \tab : \tab :\tab :\cr
#' : \tab : \tab :\tab :\cr
#' }
#'
#'
#'
#'
#'\strong{\emph{----------------------------------- Truth ------------------------------------------}}
#'
#'A sheet named \strong{Truth } consists of three columns
#' \strong{\emph{ precisely }} named from the right
#' hand side:\strong{CaseID, LesionID, Weight} .
#'
#'\strong{An Example of a sheet named Truth in a \code{.xlsx} file for the Jafroc software}
#'
#'
#'\strong{ Interpretation of table}
#'
#'For example, the first image (CaseID = 1) contains
#'three lesions each of which is named 1,2,3, namely LesionID = 1,2,3.
#'For example, the second image (CaseID = 2) contains
#'two lesions each of which is named 1,2, namely LesionID = 1,2.
#'For example, the third image (CaseID = 3) contains
#'a sinle lesion named 1, namely LesionID = 1.
#'
#' \tabular{ccc}{
#' CaseID \tab LesionID \tab Weight\cr
#' -------------------\tab-------------------\tab------------------\cr
#' 1 \tab 1 \tab 0.3333...\cr
#' 1 \tab 2 \tab 0.3333...\cr
#' 1 \tab 3 \tab 0.3333...\cr
#' 2 \tab 1 \tab 0.5\cr
#' 2 \tab 2 \tab 0.5\cr
#' 3 \tab 1 \tab 1\cr
#' 4 \tab 1 \tab 0.25\cr
#' 4 \tab 2 \tab 0.25\cr
#' 4 \tab 3 \tab 0.25\cr
#' 4 \tab 4 \tab 0.25\cr
#' 5 \tab 1 \tab 0.5\cr
#' 5 \tab 2 \tab 0.5\cr
#' 6 \tab 1 \tab 0.3333...\cr
#' 6 \tab 2 \tab 0.3333...\cr
#' 6 \tab 3 \tab 0.3333...\cr
#' 7 \tab 1 \tab 0.3333...\cr
#' 7 \tab 2 \tab 0.3333...\cr
#' 7 \tab 3 \tab 0.3333...\cr
#' 8 \tab 1 \tab 0.25\cr
#' 8 \tab 2 \tab 0.25\cr
#' 8 \tab 3 \tab 0.25\cr
#' 8 \tab 4 \tab 0.25\cr
#' : \tab :\tab :\cr
#' : \tab :\tab :\cr
#' }
#'
#'
#'
#'
#' Note that the weght are used
#' such that each image influences
#' a same effect on the esimates.
#' Without weight, the images including
#' many targets (lesions) will have very strong effect
#' on the estimates.
#' To avoid such bias, Jafroc uses weight.
#' In another context,
#' weight would be used to specify more important lesions in each image.
#'
#'
#' In this package, we do not use the information of weight.
#'Since the theory of the author of this package did not consider such weight.
#' In the future I have to include the notion of weight.
#'Jafroc use the notion fo figure of metric as non parametric manner.
#'So, it seems difficult to include it in the Bayesian model,
#'since generally speaking, Bayesian methodology is parametric.
#'
#'@import readxl
#'
#'@seealso
#'Rjafroc, which is unfortunately not on CRAN, now 2019 Jun 19.
#' Or JAFROC software in the Chakarboty's Web page.
#' Unfortunately, this software is no longer supported.
#'
#'@examples
#'
# \dontrun{
#'## Only run examples in interactive R sessions
#'if (interactive()) {
# ####1#### ####2#### ####3#### ####4#### ####5#### ####6#### ####7#### ####8#### ####9####
#'#========================================================================================
#'# Example for convert the Jafroc data
#'#========================================================================================
#'
#'
#' # Work Flow
#'
#'
#' # step 0) Make an .xlsx file "JafrocDatasetExample.xlsx" as example data
#' # to be passed to the function by an interactive manner.
#' # step 1) Convert the "JafrocDatasetExample.xlsx" file to an R object
#'
#'
#' # In the following code, when dialog box asks users to select a file, then
#' # Select "JafrocDatasetExample.xlsx" in working directory.
#'
#'
#'
#'
#' # This is important, so I write twice, again.
#'
#'
#' # In the following code, when dialog box asks you to select a file, then
#' # Select "JafrocDatasetExample.xlsx" in Working directory.
#'
#'
#'
#' # In the following code, when dialog box asks you to select a file, then
#' # Select "JafrocDatasetExample.xlsx" in Working directory.
#'
#'
#'
#'
#'
#'
#'#========================================================================================
#'# step 0) Make a Jafroc data as an example dataset
#'#========================================================================================
#'
#'# If you can find the xlsx file named JAFROC_data.xlsx
#'# in the director "inst/extdata" of this package,
#'# Then this step 0) is redundant. The author prepares this example for the people who
#'# cannot find the xlsx file in the "inst/extdata" of this package.
#' # Using an xlsx file ( which is named JAFROC_data.xlsx and
#' # included in the director "inst/extdata" of this package,)
#' # we can reconstruct it as follows:(If someone can obtain the Excel file
#' # from the path tpfp/inst/extdata/JAFROC_data.xlsx, then the following code
#' # is not required to run, because the same xlsx file is created.
#'
#'
#'
#'
#' Truth <- readxl::read_excel(system.file("extdata",
#' "JAFROC_data.xlsx",
#' package="tpfp"),
#' sheet = "Truth")
#'##### utils::View(Truth)
#'
#' TP <- readxl::read_excel( system.file("extdata",
#' "JAFROC_data.xlsx",
#' package="tpfp"),
#' sheet = "TP")
#'#### utils::View(TP)
#'
#' FP <- readxl::read_excel( system.file("extdata",
#' "JAFROC_data.xlsx",
#' package="tpfp"),
#' sheet = "FP")
#'#### utils::View(FP)
#'
#'
#'
#'
#'
#'sample <- list(TP=TP,FP=FP,Truth=Truth)
#'openxlsx::write.xlsx(sample,"JafrocDatasetExample.xlsx")
#'
#' tcltk::tkmessageBox(
#' message="A file named
#'
#' JafrocDatasetExample.xlsx
#'
#'
#' is created in the working directory")
#'
#'
#'# Now, we obtain an excel file named "JafrocDatasetExample.xlsx", which is same as
#'# the JAFROC_data.xlsx.
#'# whose format is available in the Jafroc software developed by Chakraborty.
#'# Note that if you have an excel file
#'# which is formulated correctolly for our package,
#'# then the above process does not be required.
#'
#'
#'
#'
#'# (0) From the above, we obtain "JafrocDatasetExample.xlsx"
#'# which is the multiple reader and multiple modality dataset
#'# for Jafroc analysis which is Chakraborty's software Jafroc or the R package Rjafroc
#'# but NOT implemented in our package,.
#'
#'
#'
#'
#'#========================================================================================
#'# step 1) Convert the Jafroc data obtained in the above
#'#========================================================================================
#'
#'# (1) Using "JafrocDatasetExample.xlsx" as an example excel file,
#'# we run the function to convert the excel file from Jafroc format
#'# to our format:
#'
#'
#' dataList <- convertFromJafroc(
#' No.of.Modalities =5,
#' No.of.readers =4,
#' No.of.confidence.levels = 5
#' )
#'
#'
#'
#'# In the variable, there is no xlsx files, since it is selected by interactive manner.
#'# So, please select the xlsx file, i.e., "JafrocDatasetExample.xlsx" obtained
#'# in step 0) or your own Jafroc .xlsx file.
#'
#'
#'}### Only run examples in interactive R sessions
#'
#'
# }#\dontrun
#'
#'
#' @export
#' @export convertFromJafroc
convertFromJafroc <- function(
No.of.Modalities,
No.of.readers,
No.of.confidence.levels){
M <- No.of.Modalities
Q <- No.of.readers
C <- No.of.confidence.levels
########### PASS ########## START
message("* Please select a xlsx file to calculate its number of false alarms and true positives for each confidenve level.")
message("* The lowest confidence level should be 1, not zero.")
tcltk::tkmessageBox(message="
* Select a Jafroc file whose extesion is .xlsx to be converted into an R object.
* The return.value is used to calculate FROC bayesian model in this package by running the following code:
fit_Bayesian_FROC(return.value)
")
path<-tcltk::tclvalue(tcltk::tkgetOpenFile())
if (!nchar(path)){
tcltk::tkmessageBox(message="* No file was selected! \n* Exit. \n\n\n* I love you! \n\n\n* You are not alone,... \n perhaps,...\n piggy is with you \n or doggy and doggy are with you,... ")
return(message("Suspend a process."))
}else{
xlsxfilename <- basename(path)
tcltk::tkmessageBox(message=paste("* The selected file name:",xlsxfilename,".
* More precisely, its path is the follwing:\n", path, "."))
}
# path
xlsxfilename <- basename(path)
directoryname <- dirname(path)
########### PASS ########## Fin
message("\n \n \n \n Wait...")
fileTP<-xlsx::read.xlsx(path, sheetIndex ="TP")
message("\n Wait...")
fileFP<-xlsx::read.xlsx(path, sheetIndex ="FP")
message("\n Wait... \n\n\n")
fileTruth<-xlsx::read.xlsx(path, sheetIndex ="Truth")
# head(fileTP )
# head(fileFP )
# head(fileTruth)
# names(fileTP)
# names(fileFP )
# names(fileTruth)
#
# C <- 5
# M<-5
# Q<-4
# M <-max(max(fileTP$ModalityID),max( fileFP$ModalityID))
# M <-as.integer (max( max( fileFP$ModalityID)))
# Q <-as.integer (max( max( fileTP$ReaderID )))
SRSC <-array(0,list(M,Q,C))
hhh <-array(0,list(M,Q,C))
fff <-array(0,list(M,Q,C))
for (iii in 1:nrow(fileTP)) {
hhh[ fileTP$ModalityID[iii] , fileTP$ReaderID[iii] ,fileTP$TP_Rating[iii] ] <- 1+ hhh[ fileTP$ModalityID[iii] , fileTP$ReaderID[iii] ,fileTP$TP_Rating[iii] ]
}
for (iii in 1:nrow(fileFP)) {
fff[ fileFP$ModalityID[iii] , fileFP$ReaderID[iii] ,fileFP$FP_Rating[iii] ] <- 1+ fff[ fileFP$ModalityID[iii] , fileFP$ReaderID[iii] ,fileFP$FP_Rating[iii] ]
}
######################################################
m<- rep(1:M, each=C*Q)
m
q <- rep(seq(1,Q,1), M, each=C)
q
c <-rep(rep(C:1), M*Q)
c
f <-array(0,list(M*Q*C))
f
h <-array(0,list(M*Q*C))
h
#
# length(m)
# length(q)
# length(c)
# length(h)
# length(f)
for (mmm in 1:M) {
for (qqq in 1:Q) {
for (ccc in 1:C) {
h[(mmm-1)*Q*C+(qqq-1)*C+(C+1-ccc)] <-hhh[mmm,qqq,ccc]
f[(mmm-1)*Q*C+(qqq-1)*C+(C+1-ccc)] <-fff[mmm,qqq,ccc]
}}}
# length(m)
# length(q)
# length(c)
# length(h)
# length(f)
yourdata <- data.frame(ModalityID=m,
ReaderID=q,
Confidence_lebels=c,
True_Positives=h,
False_Positives=f)
# yourdata
# nrow(yourdata)
# nrow(yourdata)==M*Q*C
message("\n-------------------------------------------------- \n\n")
message(paste("* An excel file named converted_",xlsxfilename," is made in the directory (",directoryname,"), in which the number of false positives and true positives are calcultated for your data (",xlsxfilename ,"). \n \n ",sep = ""))
xlsx::write.xlsx(yourdata, file=paste(directoryname,"/converted_",xlsxfilename,sep=""), sheetName="TPsFPs", row.names=F)
tcltk::tkmessageBox(
message=paste("
* An excel file named
converted_",xlsxfilename,"
is made in the directory
",directoryname,"
in which the number of false positives and true positives are calcultated for the
",xlsxfilename ,". \n \n ",sep = ""))
#xlsx::write.xlsx(yourdata, file=paste(directoryname,"/trans... This / is important!! This is it in path !!
# data.transformed <<- read.xlsx2(paste("transformed",xlsxfilename), 1, stringsAsFactors=FALSE)
# message("2) A dataframe is made and named 'dataframe.transformed'. \n")
# message("-------------------------------------------------- \n")
NL <- nrow(fileTruth)- nrow(subset(fileTruth ,fileTruth$LesionID ==0) )
dataList <- list(m=m,q=q,c=c,h=h,f=f, NL=NL, C=C,M=M,Q=Q)
# assign("dataList", dataList, envir=globalenv())
message("* I love you 2019. Aug. 1. so,... please please me! \n\n")
message("* The retrun value is a list object which is available for model fitting by the fit_Bayesian_FROC(). \n\n")
message("* If forget to restore the return value, then use the R object [ .Last.value ] in which the disired return value is retained. \n\n")
message("* For example, execute the R script [ fit <- fit_Bayesian_FROC(.Last.value ) ] in which the disired analysis will be done. \n\n")
message("-------------------------------------------------- \n")
return(dataList)
#
# dataList.Chakra.Web$m==dataList.transformed$m
# dataList.Chakra.Web$q==dataList.transformed$q
# dataList.Chakra.Web$c==dataList.transformed$c
# dataList.Chakra.Web$h==dataList.transformed$h
# dataList.Chakra.Web$f==dataList.transformed$f
# dataList.Chakra.Web$NL==dataList.transformed$NL
# dataList.Chakra.Web$C==dataList.transformed$C
# dataList.Chakra.Web$M==dataList.transformed$M
# dataList.Chakra.Web$Q==dataList.transformed$Q
#
# m <- data.transformed$ModalityID
# q <- data.transformed$ReaderID
# c <- data.transformed$Confidence_lebels
# h <- data.transformed$True_Positives
# f <- data.transformed$False_Positives
#
#
# length(m)
# length(q)
# length(c)
# length(h)
# length(f)
# N <-M*Q*C
#
#
# m <-data$m
# q <-data$q
# c <-data$c
# h <-data$h
# f <-data$f
# NL<-data$NL
# C<-data$C
# M<-data$M
# Q<-data$Q
#
}
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Defines functions convertFromJafroc convertFromJafrocCasewise
Documented in convertFromJafroc convertFromJafrocCasewise
#' @title Convert \code{.xlsx} File
#' of \strong{\emph{Jafroc}}
#' into \R object
#'
#'@description
#'\strong{\emph{Convert an FROC dataset}}
#'\describe{
#'\item{ \strong{\emph{from}} }{ \code{.xlsx} file of \strong{\emph{Jafroc}} }
#'\item{ \strong{\emph{into}} }{ \R object }
#'}
#'
#'
#'@details
# Create a dataset to be passed into the
# function \code{ \link{fit_Bayesian_FROC}}.
#'Convert an Excel file whose extension
#'is \code{.xlsx} of Jafroc format to
#' an \R object representing FROC data.
# to which we will apply functions in
# this package such as \code{\link{fit_Bayesian_FROC}()}.
#'
#'@param No.of.Modalities A positive integer, indicating the number of \emph{modalities} for FROC data-set in \code{.xlsx} file.
#'@param No.of.readers A positive integer, indicating the number of \emph{readers} for FROC data-set in an \code{.xlsx} file.
#'@param No.of.confidence.levels A positive integer, indicating the number of \emph{confidence levels} for FROC data-set in \code{.xlsx} file.
#'@param debug A logical, whether, inner objects in function is push out into global environment or not.
#'@references Bayesian Models for Free-response
#' Receiver Operating Characteristic Analysis,pre-print
#'@return A list, representing FROC data.
#'@details
#' The return values include a
#' list which can be passed to the function \code{fit_Bayesian_FROC}.
#' For data of Jafroc, running this function,
#' we immediately can fit the author's Bayesian
#' FROC model to this return value.
#'
#' The Jafroc software's format consists of
#' suspicious locations marked by readers and true locations.
#' Such data is \emph{redundant} for our
#' Bayesian statistical models.
#' So, we reduce the information of data to the number of
#' false positives and number of hits for
#' each confidence levels by this function.
#'
#'
#'
#' Data can be calculated from the following
#' Jafroc data, in which there are more information than TP and FP.
#' In fact, in the Jafroc data, the FP and TP are counted for each images, each lesions etc.
#' So, it has more information.
#'
#'
#' It causes limitation of our model.
#' So, our model start to fit a model to
#' the reduced data from Jafroc. So,
#' the redunction will cause the non accuracy
#' evaluation of the observer performance.
#' The future research I should start the
#' Jafroc formulation.
#'
#'
#'
#'@format The \code{.xlsx} file of Jafroc
#'consists of three sheets named \strong{\emph{TP, FP, Truth}}, \strong{\emph{ precisely! Correctly! }}
#'(other names never be permitted !!)
#'
#'
#'\strong{\emph{----------------------------------- TP ------------------------------------------}}
#'
#'
#'A sheet named \strong{\emph{TP}} consists of five columns \strong{\emph{ precisely }} named from the right hand side:
#'
#'\strong{\emph{ReaderID, ModalityID, CaseID, LesionID, TP_Rating.}}
#'
#'
#'\strong{\emph{NOTE.}}
#'\describe{
#'\item{ \strong{\emph{CaseID}} }{ Note that the above word \strong{\emph{CaseID}} means the Image ID vectors indicating the ID of radiographs. That is "case = image = radiograph".}
#'\item{ \strong{\emph{the first row}} }{ Note that the first row of each sheat of \code{.xlsx} file is constructed by the names of column as follows: }
#'}
#'
#'\strong{An Example of the sheet named \emph{TP} in a \emph{\code{.xlsx}} file for the \emph{Jafroc} software}
#'
#'
#'\strong{ Interpretation of table}
#'
#' Throughout this explanation, we follow the convention that readers are male.
#'
#' For example,
#' the first row means
#' the first reader (ReaderID=1) correctly find
#' the first lesion (LesionID = 1)
#' in the first image (CaseID = 1)
#' taken by the first modality (ModalityID = 1)
#' with his rating 5 (TP_Rating =5).
#'
#' Similarly,
#' the second row means
#' the first reader (ReaderID=1) correctly find
#' the 4-th lesion (LesionID = 4)
#' in the second image (CaseID = 2)
#' taken by the 2-nd modality (ModalityID = 2)
#' with his rating 4 (TP_Rating = 4).
#'
#'
#'
#' \tabular{ccccc}{
#' ReaderID \tab ModalityID \tab CaseID \tab LesionID \tab TP_Rating.\cr
#' -------------------\tab-------------------\tab-------------------\tab-------------------\tab------------------\cr
#' 1 \tab 1 \tab 1 \tab 1\tab 5\cr
#' 1 \tab 2 \tab 2 \tab 1\tab 4\cr
#' 1 \tab 3 \tab 4 \tab 1\tab 5\cr
#' 1 \tab 1 \tab 8 \tab 1\tab 3\cr
#' 1 \tab 2 \tab 8 \tab 2\tab 4\cr
#' 1 \tab 3 \tab 9 \tab 1\tab 4\cr
#' 1 \tab 1 \tab 9 \tab 2\tab 3\cr
#' 1 \tab 2 \tab 9 \tab 3\tab 5\cr
#' 1 \tab 3 \tab 11 \tab 1\tab 3\cr
#' 2 \tab 1 \tab 1 \tab 1\tab 4\cr
#' 2 \tab 2 \tab 4 \tab 1\tab 4\cr
#' 2 \tab 3 \tab 5 \tab 1\tab 4\cr
#' 2 \tab 1 \tab 8 \tab 1\tab 1\cr
#' 2 \tab 2 \tab 8 \tab 2\tab 2\cr
#' 2 \tab 3 \tab 8 \tab 3\tab 2\cr
#' 2 \tab 1 \tab 10 \tab 1\tab 3\cr
#' 2 \tab 2 \tab 10 \tab 2\tab 2\cr
#' 2 \tab 3 \tab 11 \tab 1\tab 2\cr
#'
#' : \tab : \tab : \tab :\tab :\cr
#' : \tab : \tab : \tab :\tab :\cr }
#'
#'
#'\strong{\emph{----------------------------------- FP ------------------------------------------}}
#'
#'
#'
#' A sheet named \strong{FP} consists of four columns \strong{\emph{ precisely }}
#' named from the right hand side: \strong{ReaderID, ModalityID, CaseID, FP_Rating}
#'\strong{An Example of a sheet named FP in a \code{.xlsx} file for the Jafroc software}
#'
#'
#'
#'
#'
#'
#'\strong{ Interpretation of table}
#'
#'
#' For example,
#' the first row means
#' the first reader (ReaderID=1) makes
#' a false alarm location
#' in the first image (CaseID = 1)
#' taken by the first modality (ModalityID = 1)
#' with his rating 2 (TP_Rating =2).
#'
#' Similarly,
#' the second row means
#' the first reader (ReaderID=1) makes
#' a false alarm location
#' in the second image (CaseID = 2)
#' taken by the 2-nd modality (ModalityID = 2)
#' with his rating 1 (TP_Rating = 1).
#'
#'
#' Similarly,
#' the 6-th and 7-th rows mean that
#' the first reader (ReaderID=1) makes
#' two false alarm location
#' in the second patient (CaseID = 2).
#' The first false alarm is in the image
#' taken by the 1-st modality (ModalityID = 1)
#' with his rating 1 (TP_Rating = 1).
#' The second false alarm is in the image
#' taken by the 3-rd modality (ModalityID = 3)
#' with his rating 2 (TP_Rating = 2).
#'
#'
#'
#'
#'
#'
#' \tabular{cccc}{
#' ReaderID \tab ModalityID \tab CaseID \tab FP_Rating.\cr
#' -------------------\tab-------------------\tab-------------------\tab------------------\cr
#' 1 \tab 1 \tab 1 \tab 2\cr
#' 1 \tab 2 \tab 2 \tab 1\cr
#' 1 \tab 3 \tab 3 \tab 1\cr
#' 1 \tab 1 \tab 5 \tab 2\cr
#' 1 \tab 2 \tab 7 \tab 1\cr
#' 1 \tab 3 \tab 7 \tab 2\cr
#' 1 \tab 1 \tab 9 \tab 3\cr
#' 1 \tab 2 \tab 9 \tab 4\cr
#' 1 \tab 3 \tab 10 \tab 1\cr
#' 2 \tab 1 \tab 1 \tab 2\cr
#' 2 \tab 2 \tab 2 \tab 3\cr
#' 2 \tab 3 \tab 3 \tab 4\cr
#' 2 \tab 1 \tab 8 \tab 1\cr
#' 2 \tab 2 \tab 9 \tab 1\cr
#' 2 \tab 3 \tab 11 \tab 1\cr
#' 2 \tab 1 \tab 14 \tab 1\cr
#' 2 \tab 2 \tab 15 \tab 1\cr
#' 2 \tab 3 \tab 21 \tab 2\cr
#' : \tab : \tab :\tab :\cr
#' : \tab : \tab :\tab :\cr
#' }
#'
#'
#'
#'
#'\strong{\emph{----------------------------------- Truth ------------------------------------------}}
#'
#'A sheet named \strong{Truth } consists of three columns
#' \strong{\emph{ precisely }} named from the right
#' hand side:\strong{CaseID, LesionID, Weight} .
#'
#'\strong{An Example of a sheet named Truth in a \code{.xlsx} file for the Jafroc software}
#'
#'
#'\strong{ Interpretation of table}
#'
#'For example, the first image (CaseID = 1) contains
#'three lesions each of which is named 1,2,3, namely LesionID = 1,2,3.
#'For example, the second image (CaseID = 2) contains
#'two lesions each of which is named 1,2, namely LesionID = 1,2.
#'For example, the third image (CaseID = 3) contains
#'a sinle lesion named 1, namely LesionID = 1.
#'
#' \tabular{ccc}{
#' CaseID \tab LesionID \tab Weight\cr
#' -------------------\tab-------------------\tab------------------\cr
#' 1 \tab 1 \tab 0.3333...\cr
#' 1 \tab 2 \tab 0.3333...\cr
#' 1 \tab 3 \tab 0.3333...\cr
#' 2 \tab 1 \tab 0.5\cr
#' 2 \tab 2 \tab 0.5\cr
#' 3 \tab 1 \tab 1\cr
#' 4 \tab 1 \tab 0.25\cr
#' 4 \tab 2 \tab 0.25\cr
#' 4 \tab 3 \tab 0.25\cr
#' 4 \tab 4 \tab 0.25\cr
#' 5 \tab 1 \tab 0.5\cr
#' 5 \tab 2 \tab 0.5\cr
#' 6 \tab 1 \tab 0.3333...\cr
#' 6 \tab 2 \tab 0.3333...\cr
#' 6 \tab 3 \tab 0.3333...\cr
#' 7 \tab 1 \tab 0.3333...\cr
#' 7 \tab 2 \tab 0.3333...\cr
#' 7 \tab 3 \tab 0.3333...\cr
#' 8 \tab 1 \tab 0.25\cr
#' 8 \tab 2 \tab 0.25\cr
#' 8 \tab 3 \tab 0.25\cr
#' 8 \tab 4 \tab 0.25\cr
#' : \tab :\tab :\cr
#' : \tab :\tab :\cr
#' }
#'
#'
#'
#'
#' Note that the weght are used
#' such that each image influences
#' a same effect on the esimates.
#' Without weight, the images including
#' many targets (lesions) will have very strong effect
#' on the estimates.
#' To avoid such bias, Jafroc uses weight.
#' In another context,
#' weight would be used to specify more important lesions in each image.
#'
#' In this package, we do not use the information of weight.
#'Since the theory of the author of this package did not consider such weight.
#' In the future I have to include the notion of weight.
#'Jafroc use the notion fo figure of metric as non parametric manner.
#'So, it seems difficult to include it in the Bayesian model,
#'since generally speaking, Bayesian methodology is parametric.
#'
#'@import readxl
#'
#'@seealso
#'Rjafroc, which is unfortunately not on CRAN, now 2019 Jun 19.
#' Or JAFROC software in the Chakarboty's Web page.
#' Unfortunately, this software is no longer supported.
#'
#'@examples
#'
# \dontrun{
#'## Only run examples in interactive R sessions
#'if (interactive()) {
# ####1#### ####2#### ####3#### ####4#### ####5#### ####6#### ####7#### ####8#### ####9####
#'#========================================================================================
#'# Example for convert the Jafroc data
#'#========================================================================================
#'
#' # Work Flow
#'
#' # step 0) Make an .xlsx file "JafrocDatasetExample.xlsx" as example data
#' # to be passed to the function by interactive manner.
#' # step 1) Convert the "JafrocDatasetExample.xlsx" file to an R object
#'
#'
#'
#'
#'
#'
#'
#'
#'
#'
#'
#'
#'
#' # In the following code, when dialog box asks you to select a file, then
#' # Select "JafrocDatasetExample.xlsx" in Working directory.
#'
#'
#' # This is important, so I write twice, again.
#'
#'
#' # In the following code, when dialog box asks you to select a file, then
#' # Select "JafrocDatasetExample.xlsx" in Working directory.
#'
#'
#'
#' # In the following code, when dialog box asks you to select a file, then
#' # Select "JafrocDatasetExample.xlsx" in Working directory.
#'
#'
#'
#'
#'
#'#========================================================================================
#'# step 0) Make a Jafroc data as an example dataset
#'#========================================================================================
#'
#'# If you can find the xlsx file named JAFROC_data.xlsx
#'# in the director "inst/extdata" of this package,
#'# Then this step 0) is redundant. The author prepares this example for the people who
#'# cannot find the xlsx file in the "inst/extdata" of this package.
#' # Using an xlsx file ( which is named JAFROC_data.xlsx and
#' # included in the director "inst/extdata" of this package,)
#' # we can reconstruct it as follows:(If someone can obtain the Excel file
#' # from the path tpfp/inst/extdata/JAFROC_data.xlsx, then the following code
#' # is not required to run, because the same xlsx file is created.
#'
#'
#'
#'
#' Truth <- readxl::read_excel(system.file("extdata",
#' "JAFROC_data.xlsx",
#' package="tpfp"),
#' sheet = "Truth")
#'##### utils::View(Truth)
#'
#' TP <- readxl::read_excel( system.file("extdata",
#' "JAFROC_data.xlsx",
#' package="tpfp"),
#' sheet = "TP")
#'#### utils::View(TP)
#'
#' FP <- readxl::read_excel( system.file("extdata",
#' "JAFROC_data.xlsx",
#' package="tpfp"),
#' sheet = "FP")
#'#### utils::View(FP)
#'
#'
#'
#'
#'
#'sample <- list(TP=TP,FP=FP,Truth=Truth)
#'openxlsx::write.xlsx(sample,"JafrocDatasetExample.xlsx")
#'
#' tcltk::tkmessageBox(
#' message="A file named
#'
#' JafrocDatasetExample.xlsx
#'
#'
#' is created in the working directory")
#'
#'
#'# Now, we obtain an excel file named "JafrocDatasetExample.xlsx", which is same as
#'# the JAFROC_data.xlsx.
#'# whose format is available in the Jafroc software developed by Chakraborty.
#'# Note that if you have an excel file
#'# which is formulated correctolly for our package,
#'# then the above process does not be required.
#'
#'
#'
#'
#'# (0) From the above, we obtain "JafrocDatasetExample.xlsx"
#'# which is the multiple reader and multiple modality dataset
#'# for Jafroc analysis which is Chakraborty's software Jafroc or the R package Rjafroc
#'# but NOT implemented in our package,.
#'
#'
#'
#'
#'#========================================================================================
#'# step 1) Convert the Jafroc data obtained in the above
#'#========================================================================================
#'
#'# (1) Using "JafrocDatasetExample.xlsx" as an example excel file,
#'# we run the function to convert the excel file from Jafroc format
#'# to our format:
#'
#'
#' dataList <- convertFromJafrocCasewise(
#' No.of.Modalities =5,
#' No.of.readers =4,
#' No.of.confidence.levels = 5
#' )
#'
#'
#'
#'# In the variable, there is no xlsx files, since it is selected by interactive manner.
#'# So, please select the xlsx file, i.e., "JafrocDatasetExample.xlsx" obtained
#'# in step 0) or your own Jafroc .xlsx file.
#'
#'
#'}### Only run examples in interactive R sessions
#'
#'
# }#' \dontrun{
#'
#'
#' @export
convertFromJafrocCasewise <- function(
No.of.Modalities,
No.of.readers,
No.of.confidence.levels,
# No.of.images
debug = FALSE
){
M <- No.of.Modalities
Q <- No.of.readers
C <- No.of.confidence.levels
# NI <- No.of.images
########### PASS ########## START
message("* Please select a xlsx file to calculate its number of false alarms and true positives for each confidenve level.")
message("* The lowest confidence level should be 1, not zero.")
tcltk::tkmessageBox(message="
* Select a Jafroc file whose extesion is .xlsx to be converted into an R object.
* The return.value is used to calculate FROC bayesian model in this package by running the following code:
fit_Bayesian_FROC(return.value)")
path<-tcltk::tclvalue(tcltk::tkgetOpenFile())
if (!nchar(path)){
tcltk::tkmessageBox(
message="* No file was selected! \n* Exit. \n\n\n* I love you! \n\n\n* You are not alone,... \n perhaps,...\n piggy is with you \n or doggy and doggy are with you,... ")
return(message("Suspend a process."))
}else{
xlsxfilename <- basename(path)
tcltk::tkmessageBox(message=paste("* The selected file name:",xlsxfilename,".
* More precisely, its path is the follwing:\n", path, "."))
}
# path
xlsxfilename <- basename(path)
directoryname <- dirname(path)
########### PASS ########## Fin
message("\n \n \n \n Wait...")
fileTP<-xlsx::read.xlsx(path, sheetIndex ="TP")
message("\n Wait...")
fileFP<-xlsx::read.xlsx(path, sheetIndex ="FP")
message("\n Wait... \n\n\n")
fileTruth<-xlsx::read.xlsx(path, sheetIndex ="Truth")
if(min(fileTruth$CaseID) ==0) NI <- max(fileTruth$CaseID) +1
if(min(fileTruth$CaseID) ==1) NI <- max(fileTruth$CaseID) +0
if(min(fileTruth$CaseID) ==2) NI <- max(fileTruth$CaseID) -1
if(min(fileTruth$CaseID) ==3) NI <- max(fileTruth$CaseID) -2
minimal_CaseID <- min(fileTruth$CaseID)
maximal_CaseID <- max(fileTruth$CaseID)
# caseID_deseased -----------
for (iii in 1:nrow(fileTruth)) {
fileTruth_deseased_only <- fileTruth[ !(fileTruth$LesionID == 0),]
# caseIDs_deseased <- fileTruth[ !(fileTruth$LesionID == 0),]
}
caseIDs_deseased <- unique(fileTruth_deseased_only$CaseID)
# number_of_caseIDs_deseased
# NL_casewise----
NL_casewise <-array(0,c(NI))
for (caseid in minimal_CaseID:maximal_CaseID) {
NL_casewise[caseid] <- max(fileTruth[fileTruth$CaseID==caseid,]$LesionID)
}
if(debug){
my_global_env <- globalenv()
my_global_env$NL_casewise <- NL_casewise
print.data.frame( data.frame( NL_casewise= NL_casewise))
}
# head(fileTP )
# head(fileFP )
# head(fileTruth)
# names(fileTP)
# names(fileFP )
# names(fileTruth)
#
# C <- 5
# M<-5
# Q<-4
# M <-max(max(fileTP$ModalityID),max( fileFP$ModalityID))
# M <-as.integer (max( max( fileFP$ModalityID)))
# Q <-as.integer (max( max( fileTP$ReaderID )))
if( min( c( fileTP$CaseID, fileTP$CaseID, fileTruth$CaseID) )==0 ) NI <- max(c( fileTP$CaseID, fileTP$CaseID, fileTruth$CaseID) ) +1
if( min( c( fileTP$CaseID, fileTP$CaseID, fileTruth$CaseID) )==1 ) NI <- max(c( fileTP$CaseID, fileTP$CaseID, fileTruth$CaseID) )
SRSC <-array(0,c(NI,M,Q,C))
harray <-array(0,c(NI,M,Q,C))
farray <-array(0,c(NI,M,Q,C))
# if( min( c( fileTP$CaseID, fileTP$CaseID, fileTruth$CaseID) )==1 ){
for (iii in 1:nrow(fileTP)) {
harray[ fileTP$CaseID[iii] , fileTP$ModalityID[iii] , fileTP$ReaderID[iii] ,fileTP$TP_Rating[iii] ] <- 1+ harray[ fileTP$CaseID[iii] , fileTP$ModalityID[iii] , fileTP$ReaderID[iii] ,fileTP$TP_Rating[iii] ]
}
for (iii in 1:nrow(fileFP)) {
farray[ fileFP$CaseID[iii] , fileFP$ModalityID[iii] , fileFP$ReaderID[iii] ,fileFP$FP_Rating[iii] ] <- 1+ farray[ fileFP$CaseID[iii] , fileFP$ModalityID[iii] , fileFP$ReaderID[iii] ,fileFP$FP_Rating[iii] ]
}
# }else if( min( c( fileTP$CaseID, fileTP$CaseID, fileTruth$CaseID) )==0 ){
# for (iii in 1:nrow(fileTP)) {
# harray[ fileTP$CaseID[iii] +1 , fileTP$ModalityID[iii] , fileTP$ReaderID[iii] ,fileTP$TP_Rating[iii] ] <- 1+ harray[ fileTP$CaseID[iii] +1 , fileTP$ModalityID[iii] , fileTP$ReaderID[iii] ,fileTP$TP_Rating[iii] ]
# }
# for (iii in 1:nrow(fileFP)) {
# farray[ fileFP$CaseID[iii] +1 , fileFP$ModalityID[iii] , fileFP$ReaderID[iii] ,fileFP$FP_Rating[iii] ] <- 1+ farray[ fileFP$CaseID[iii] +1 , fileFP$ModalityID[iii] , fileFP$ReaderID[iii] ,fileFP$FP_Rating[iii] ]
# }
#
#
#
# }
# m<- rep(1:M, each=C*Q)
# m
# q <- rep(seq(1,Q,1), M, each=C)
# q
# c <-rep(rep(C:1), M*Q)
# c
a <- caseID_m_q_c_vector_from_NI_M_Q_C(NI = NI , M = M ,Q = Q, C = C )
caseID <- a$caseID
m <- a$m
c <- a$c
q <- a$q
f <-array(0,c(NI*M*Q*C))
f
h <-array(0,c(NI*M*Q*C))
h
#
# length(m)
# length(q)
# length(c)
# length(h)
# length(f)
for(case in 1:NI)
for (mmm in 1:M) {
for (qqq in 1:Q) {
for (ccc in 1:C) {
h[ (case -1 )*M*Q*C + (mmm-1)*Q*C+(qqq-1)*C+(C+1-ccc)] <-harray[case,mmm,qqq,ccc]
f[ (case -1 )*M*Q*C + (mmm-1)*Q*C+(qqq-1)*C+(C+1-ccc)] <-farray[case,mmm,qqq,ccc]
}}}
# length(m)
# length(q)
# length(c)
# length(h)
# length(f)
yourdata <- data.frame(CaseID = caseID,
ModalityID=m,
ReaderID=q,
Confidence_lebels=c,
True_Positives=h,
False_Positives=f)
print.data.frame(yourdata,row.names = F)
if(debug){
# my_global_env <- globalenv()
my_global_env$h<-h
my_global_env$f<-f
my_global_env$harray<-harray
my_global_env$farray<-farray
my_global_env$h<-h
my_global_env$f<-f
my_global_env$M<-M
my_global_env$Q<-Q
my_global_env$C<-C
my_global_env$m<-m
my_global_env$q<-q
my_global_env$c<-c
my_global_env$n<-nrow(fileTP)
my_global_env$NI<-NI
length(h);length(f); length(q);length(m)
my_global_env$fileTP<-fileTP
my_global_env$fileFP<-fileFP
my_global_env$fileTruth<-fileTruth
my_global_env$yourdata<-yourdata
}
# yourdata
# nrow(yourdata)
# nrow(yourdata)==M*Q*C
message("\n \n wait .... \n")
xlsx::write.xlsx(yourdata, file=paste(directoryname,"/converted_",xlsxfilename,sep=""), sheetName="TPsFPs", row.names=F)
message(paste("* An excel file named converted_",xlsxfilename," is made in the directory (",directoryname,"), in which the number of false positives and true positives are calcultated for your data (",xlsxfilename ,"). \n \n ",sep = ""))
#here ----
# tcltk::tkmessageBox(
# message=paste("
# * An excel file named
#
#
# converted_",xlsxfilename,"
#
# is made in the directory
#
# ",directoryname,"
#
# in which the number of false positives and true positives are calcultated for the
#
# ",xlsxfilename ,". \n \n ",sep = "")
# )
message("
* An excel file named
converted_",xlsxfilename,"
is made in the directory
",directoryname,"
in which the number of false positives and true positives are calcultated for the
",xlsxfilename ,". \n \n ",sep = "")
#xlsx::write.xlsx(yourdata, file=paste(directoryname,"/trans... This / is important!! This is it in path !!
# data.transformed <<- read.xlsx2(paste("transformed",xlsxfilename), 1, stringsAsFactors=FALSE)
# message("2) A dataframe is made and named 'dataframe.transformed'. \n")
# message("-------------------------------------------------- \n")
NL <- nrow(fileTruth)- nrow(subset(fileTruth ,fileTruth$LesionID ==0) )
# return -----
dataList <- list(caseID=caseID,
NI=NI,
NI_deseased = length(caseIDs_deseased),
NL_casewise=NL_casewise,
caseIDs_deseased=caseIDs_deseased,
fileTruth_deseased_only=fileTruth_deseased_only,
fileTP=fileTP,
fileFP=fileFP,
fileTruth=fileTruth,
m=m,q=q,c=c,h=h,f=f, NL=NL, C=C,M=M,Q=Q,
harray=harray,farray=farray,df=yourdata)
# results <- fastDummies::dummy_cols(df, select_columns = "ModalityID")
# knitr::kable(results)
# assign("dataList", dataList, envir=globalenv())
message("* I love you 2019. Aug. 1. so,... please please me! \n\n")
message("* The retrun value is a list object which is available for model fitting by the fit_Bayesian_FROC(). \n\n")
message("* If forget to restore the return value, then use the R object [ .Last.value ] in which the disired return value is retained. \n\n")
message("* For example, execute the R script [ fit <- fit_Bayesian_FROC(.Last.value ) ] in which the disired analysis will be done. \n\n")
message("-------------------------------------------------- \n")
return(dataList)
#
# dataList.Chakra.Web$m==dataList.transformed$m
# dataList.Chakra.Web$q==dataList.transformed$q
# dataList.Chakra.Web$c==dataList.transformed$c
# dataList.Chakra.Web$h==dataList.transformed$h
# dataList.Chakra.Web$f==dataList.transformed$f
# dataList.Chakra.Web$NL==dataList.transformed$NL
# dataList.Chakra.Web$C==dataList.transformed$C
# dataList.Chakra.Web$M==dataList.transformed$M
# dataList.Chakra.Web$Q==dataList.transformed$Q
#
# m <- data.transformed$ModalityID
# q <- data.transformed$ReaderID
# c <- data.transformed$Confidence_lebels
# h <- data.transformed$True_Positives
# f <- data.transformed$False_Positives
#
#
# length(m)
# length(q)
# length(c)
# length(h)
# length(f)
# N <-M*Q*C
#
#
# m <-data$m
# q <-data$q
# c <-data$c
# h <-data$h
# f <-data$f
# NL<-data$NL
# C<-data$C
# M<-data$M
# Q<-data$Q
#
}
#' @title Convert \code{.xlsx} File
#' of \strong{\emph{Jafroc}}
#' into \R object
#'
#'@description
#'\strong{\emph{Convert an FROC dataset}}
#'\describe{
#'\item{ \strong{\emph{from}} }{ \code{.xlsx} file of \strong{\emph{Jafroc}} }
#'\item{ \strong{\emph{into}} }{ \R object }
#'}
#'
#'
#'@details
# Create a dataset to be passed into the
# function \code{ \link{fit_Bayesian_FROC}}.
#'Convert an Excel file whose extension
#'is \code{.xlsx} of Jafroc format to
#' an \R object representing FROC data
# to which we will apply functions in
# this package such as \code{\link{fit_Bayesian_FROC}()}.
#'
#'Revised 2019 Jun 19
#'Revised 2019 Dec 13
#'@param No.of.Modalities A positive integer, indicating the number of \emph{modalities} for FROC data-set in \code{.xlsx} file.
#'@param No.of.readers A positive integer, indicating the number of \emph{readers} for FROC data-set in an \code{.xlsx} file.
#'@param No.of.confidence.levels A positive integer, indicating the number of \emph{confidence levels} for FROC data-set in \code{.xlsx} file.
#'@references Bayesian Models for Free-response
#' Receiver Operating Characteristic Analysis,pre-print
#'@return A list, representing FROC data.
#'@details
#' The return values include a
#' list which can be passed to the function \code{fit_Bayesian_FROC}.
#' For data of Jafroc, running this function,
#' we immediately can fit the author's Bayesian
#' FROC model to this return value.
#'
#' The Jafroc software's format consists of
#' suspicious locations marked by readers and true locations.
#' Such data is \emph{redundant} for our
#' Bayesian statistical models.
#' So, we reduce the information of data to the number of
#' false positives and number of hits for
#' each confidence levels by this function.
#'
#'
#'
#' Data can be calculated from the following
#' Jafroc data, in which there are more information than TP and FP.
#' In fact, in the Jafroc data, the FP and TP are counted for each images, each lesions etc.
#' So, it has more information.
#'
#'
#' It causes limitation of our model.
#' So, our model start to fit a model to
#' the reduced data from Jafroc. So,
#' the redunction will cause the non accuracy
#' evaluation of the observer performance.
#' The future research I should start the
#' Jafroc formulation.
#'
#'
#'
#'@format The \code{.xlsx} file of Jafroc
#'consists of three sheets named \strong{\emph{TP, FP, Truth}}, \strong{\emph{ precisely! Correctly! }}
#'(other names never be permitted !!)
#'
#'
#'\strong{\emph{----------------------------------- TP ------------------------------------------}}
#'
#'
#'A sheet named \strong{\emph{TP}} consists of five columns \strong{\emph{ precisely }} named from the right hand side:
#'
#'\strong{\emph{ReaderID, ModalityID, CaseID, LesionID, TP_Rating.}}
#'
#'
#'\strong{\emph{NOTE.}}
#'\describe{
#'\item{ \strong{\emph{CaseID}} }{ Note that the above word \strong{\emph{CaseID}} means the Image ID vectors indicating the ID of radiographs. That is "case = image = radiograph".}
#'\item{ \strong{\emph{the first row}} }{ Note that the first row of each sheat of \code{.xlsx} file is constructed by the names of column as follows: }
#'}
#'
#'\strong{An Example of the sheet named \emph{TP} in a \emph{\code{.xlsx}} file for the \emph{Jafroc} software}
#'
#'
#'\strong{ Interpretation of table}
#'
#' Throughout this explanation, we follow the convention that readers are male.
#'
#' For example,
#' the first row means
#' the first reader (ReaderID=1) correctly find
#' the first lesion (LesionID = 1)
#' in the first image (CaseID = 1)
#' taken by the first modality (ModalityID = 1)
#' with his rating 5 (TP_Rating =5).
#'
#' Similarly,
#' the second row means
#' the first reader (ReaderID=1) correctly find
#' the 4-th lesion (LesionID = 4)
#' in the second image (CaseID = 2)
#' taken by the 2-nd modality (ModalityID = 2)
#' with his rating 4 (TP_Rating = 4).
#'
#'
#'
#' \tabular{ccccc}{
#' ReaderID \tab ModalityID \tab CaseID \tab LesionID \tab TP_Rating.\cr
#' -------------------\tab-------------------\tab-------------------\tab-------------------\tab------------------\cr
#' 1 \tab 1 \tab 1 \tab 1\tab 5\cr
#' 1 \tab 2 \tab 2 \tab 1\tab 4\cr
#' 1 \tab 3 \tab 4 \tab 1\tab 5\cr
#' 1 \tab 1 \tab 8 \tab 1\tab 3\cr
#' 1 \tab 2 \tab 8 \tab 2\tab 4\cr
#' 1 \tab 3 \tab 9 \tab 1\tab 4\cr
#' 1 \tab 1 \tab 9 \tab 2\tab 3\cr
#' 1 \tab 2 \tab 9 \tab 3\tab 5\cr
#' 1 \tab 3 \tab 11 \tab 1\tab 3\cr
#' 2 \tab 1 \tab 1 \tab 1\tab 4\cr
#' 2 \tab 2 \tab 4 \tab 1\tab 4\cr
#' 2 \tab 3 \tab 5 \tab 1\tab 4\cr
#' 2 \tab 1 \tab 8 \tab 1\tab 1\cr
#' 2 \tab 2 \tab 8 \tab 2\tab 2\cr
#' 2 \tab 3 \tab 8 \tab 3\tab 2\cr
#' 2 \tab 1 \tab 10 \tab 1\tab 3\cr
#' 2 \tab 2 \tab 10 \tab 2\tab 2\cr
#' 2 \tab 3 \tab 11 \tab 1\tab 2\cr
#'
#' : \tab : \tab : \tab :\tab :\cr
#' : \tab : \tab : \tab :\tab :\cr }
#'
#'
#'\strong{\emph{----------------------------------- FP ------------------------------------------}}
#'
#'
#'
#' A sheet named \strong{FP} consists of four columns \strong{\emph{ precisely }}
#' named from the right hand side: \strong{ReaderID, ModalityID, CaseID, FP_Rating}
#'\strong{An Example of a sheet named FP in a file of extension "\code{.xlsx}" file for the Jafroc software}
#'
#'
#'
#'
#'
#'
#'\strong{ Interpretation of table}
#'
#'
#' For example,
#' the first row means that
#' the first reader (ReaderID=1) makes
#' a false alarm location
#' in the first image (CaseID = 1)
#' taken by the first modality (ModalityID = 1)
#' with his rating 2 (TP_Rating =2).
#'
#' Similarly,
#' the second row means that
#' the first reader (ReaderID=1) makes
#' a false alarm location
#' in the second image (CaseID = 2)
#' taken by the 2-nd modality (ModalityID = 2)
#' with his rating 1 (TP_Rating = 1).
#'
#'
#' Similarly,
#' the 6-th and 7-th rows mean that
#' the first reader (ReaderID=1) makes
#' two false alarm location
#' in the second patient (CaseID = 2).
#' The first false alarm is in the image
#' taken by the 1-st modality (ModalityID = 1)
#' with his rating 1 (TP_Rating = 1).
#' The second false alarm is in the image
#' taken by the 3-rd modality (ModalityID = 3)
#' with his rating 2 (TP_Rating = 2).
#'
#'
#'
#'
#'
#'
#' \tabular{cccc}{
#' ReaderID \tab ModalityID \tab CaseID \tab FP_Rating.\cr
#' -------------------\tab-------------------\tab-------------------\tab------------------\cr
#' 1 \tab 1 \tab 1 \tab 2\cr
#' 1 \tab 2 \tab 2 \tab 1\cr
#' 1 \tab 3 \tab 3 \tab 1\cr
#' 1 \tab 1 \tab 5 \tab 2\cr
#' 1 \tab 2 \tab 7 \tab 1\cr
#' 1 \tab 3 \tab 7 \tab 2\cr
#' 1 \tab 1 \tab 9 \tab 3\cr
#' 1 \tab 2 \tab 9 \tab 4\cr
#' 1 \tab 3 \tab 10 \tab 1\cr
#' 2 \tab 1 \tab 1 \tab 2\cr
#' 2 \tab 2 \tab 2 \tab 3\cr
#' 2 \tab 3 \tab 3 \tab 4\cr
#' 2 \tab 1 \tab 8 \tab 1\cr
#' 2 \tab 2 \tab 9 \tab 1\cr
#' 2 \tab 3 \tab 11 \tab 1\cr
#' 2 \tab 1 \tab 14 \tab 1\cr
#' 2 \tab 2 \tab 15 \tab 1\cr
#' 2 \tab 3 \tab 21 \tab 2\cr
#' : \tab : \tab :\tab :\cr
#' : \tab : \tab :\tab :\cr
#' }
#'
#'
#'
#'
#'\strong{\emph{----------------------------------- Truth ------------------------------------------}}
#'
#'A sheet named \strong{Truth } consists of three columns
#' \strong{\emph{ precisely }} named from the right
#' hand side:\strong{CaseID, LesionID, Weight} .
#'
#'\strong{An Example of a sheet named Truth in a \code{.xlsx} file for the Jafroc software}
#'
#'
#'\strong{ Interpretation of table}
#'
#'For example, the first image (CaseID = 1) contains
#'three lesions each of which is named 1,2,3, namely LesionID = 1,2,3.
#'For example, the second image (CaseID = 2) contains
#'two lesions each of which is named 1,2, namely LesionID = 1,2.
#'For example, the third image (CaseID = 3) contains
#'a sinle lesion named 1, namely LesionID = 1.
#'
#' \tabular{ccc}{
#' CaseID \tab LesionID \tab Weight\cr
#' -------------------\tab-------------------\tab------------------\cr
#' 1 \tab 1 \tab 0.3333...\cr
#' 1 \tab 2 \tab 0.3333...\cr
#' 1 \tab 3 \tab 0.3333...\cr
#' 2 \tab 1 \tab 0.5\cr
#' 2 \tab 2 \tab 0.5\cr
#' 3 \tab 1 \tab 1\cr
#' 4 \tab 1 \tab 0.25\cr
#' 4 \tab 2 \tab 0.25\cr
#' 4 \tab 3 \tab 0.25\cr
#' 4 \tab 4 \tab 0.25\cr
#' 5 \tab 1 \tab 0.5\cr
#' 5 \tab 2 \tab 0.5\cr
#' 6 \tab 1 \tab 0.3333...\cr
#' 6 \tab 2 \tab 0.3333...\cr
#' 6 \tab 3 \tab 0.3333...\cr
#' 7 \tab 1 \tab 0.3333...\cr
#' 7 \tab 2 \tab 0.3333...\cr
#' 7 \tab 3 \tab 0.3333...\cr
#' 8 \tab 1 \tab 0.25\cr
#' 8 \tab 2 \tab 0.25\cr
#' 8 \tab 3 \tab 0.25\cr
#' 8 \tab 4 \tab 0.25\cr
#' : \tab :\tab :\cr
#' : \tab :\tab :\cr
#' }
#'
#'
#'
#'
#' Note that the weght are used
#' such that each image influences
#' a same effect on the esimates.
#' Without weight, the images including
#' many targets (lesions) will have very strong effect
#' on the estimates.
#' To avoid such bias, Jafroc uses weight.
#' In another context,
#' weight would be used to specify more important lesions in each image.
#'
#'
#' In this package, we do not use the information of weight.
#'Since the theory of the author of this package did not consider such weight.
#' In the future I have to include the notion of weight.
#'Jafroc use the notion fo figure of metric as non parametric manner.
#'So, it seems difficult to include it in the Bayesian model,
#'since generally speaking, Bayesian methodology is parametric.
#'
#'@import readxl
#'
#'@seealso
#'Rjafroc, which is unfortunately not on CRAN, now 2019 Jun 19.
#' Or JAFROC software in the Chakarboty's Web page.
#' Unfortunately, this software is no longer supported.
#'
#'@examples
#'
# \dontrun{
#'## Only run examples in interactive R sessions
#'if (interactive()) {
# ####1#### ####2#### ####3#### ####4#### ####5#### ####6#### ####7#### ####8#### ####9####
#'#========================================================================================
#'# Example for convert the Jafroc data
#'#========================================================================================
#'
#'
#' # Work Flow
#'
#'
#' # step 0) Make an .xlsx file "JafrocDatasetExample.xlsx" as example data
#' # to be passed to the function by an interactive manner.
#' # step 1) Convert the "JafrocDatasetExample.xlsx" file to an R object
#'
#'
#' # In the following code, when dialog box asks users to select a file, then
#' # Select "JafrocDatasetExample.xlsx" in working directory.
#'
#'
#'
#'
#' # This is important, so I write twice, again.
#'
#'
#' # In the following code, when dialog box asks you to select a file, then
#' # Select "JafrocDatasetExample.xlsx" in Working directory.
#'
#'
#'
#' # In the following code, when dialog box asks you to select a file, then
#' # Select "JafrocDatasetExample.xlsx" in Working directory.
#'
#'
#'
#'
#'
#'
#'#========================================================================================
#'# step 0) Make a Jafroc data as an example dataset
#'#========================================================================================
#'
#'# If you can find the xlsx file named JAFROC_data.xlsx
#'# in the director "inst/extdata" of this package,
#'# Then this step 0) is redundant. The author prepares this example for the people who
#'# cannot find the xlsx file in the "inst/extdata" of this package.
#' # Using an xlsx file ( which is named JAFROC_data.xlsx and
#' # included in the director "inst/extdata" of this package,)
#' # we can reconstruct it as follows:(If someone can obtain the Excel file
#' # from the path tpfp/inst/extdata/JAFROC_data.xlsx, then the following code
#' # is not required to run, because the same xlsx file is created.
#'
#'
#'
#'
#' Truth <- readxl::read_excel(system.file("extdata",
#' "JAFROC_data.xlsx",
#' package="tpfp"),
#' sheet = "Truth")
#'##### utils::View(Truth)
#'
#' TP <- readxl::read_excel( system.file("extdata",
#' "JAFROC_data.xlsx",
#' package="tpfp"),
#' sheet = "TP")
#'#### utils::View(TP)
#'
#' FP <- readxl::read_excel( system.file("extdata",
#' "JAFROC_data.xlsx",
#' package="tpfp"),
#' sheet = "FP")
#'#### utils::View(FP)
#'
#'
#'
#'
#'
#'sample <- list(TP=TP,FP=FP,Truth=Truth)
#'openxlsx::write.xlsx(sample,"JafrocDatasetExample.xlsx")
#'
#' tcltk::tkmessageBox(
#' message="A file named
#'
#' JafrocDatasetExample.xlsx
#'
#'
#' is created in the working directory")
#'
#'
#'# Now, we obtain an excel file named "JafrocDatasetExample.xlsx", which is same as
#'# the JAFROC_data.xlsx.
#'# whose format is available in the Jafroc software developed by Chakraborty.
#'# Note that if you have an excel file
#'# which is formulated correctolly for our package,
#'# then the above process does not be required.
#'
#'
#'
#'
#'# (0) From the above, we obtain "JafrocDatasetExample.xlsx"
#'# which is the multiple reader and multiple modality dataset
#'# for Jafroc analysis which is Chakraborty's software Jafroc or the R package Rjafroc
#'# but NOT implemented in our package,.
#'
#'
#'
#'
#'#========================================================================================
#'# step 1) Convert the Jafroc data obtained in the above
#'#========================================================================================
#'
#'# (1) Using "JafrocDatasetExample.xlsx" as an example excel file,
#'# we run the function to convert the excel file from Jafroc format
#'# to our format:
#'
#'
#' dataList <- convertFromJafroc(
#' No.of.Modalities =5,
#' No.of.readers =4,
#' No.of.confidence.levels = 5
#' )
#'
#'
#'
#'# In the variable, there is no xlsx files, since it is selected by interactive manner.
#'# So, please select the xlsx file, i.e., "JafrocDatasetExample.xlsx" obtained
#'# in step 0) or your own Jafroc .xlsx file.
#'
#'
#'}### Only run examples in interactive R sessions
#'
#'
# }#\dontrun
#'
#'
#' @export
#' @export convertFromJafroc
convertFromJafroc <- function(
No.of.Modalities,
No.of.readers,
No.of.confidence.levels){
M <- No.of.Modalities
Q <- No.of.readers
C <- No.of.confidence.levels
########### PASS ########## START
message("* Please select a xlsx file to calculate its number of false alarms and true positives for each confidenve level.")
message("* The lowest confidence level should be 1, not zero.")
tcltk::tkmessageBox(message="
* Select a Jafroc file whose extesion is .xlsx to be converted into an R object.
* The return.value is used to calculate FROC bayesian model in this package by running the following code:
fit_Bayesian_FROC(return.value)
")
path<-tcltk::tclvalue(tcltk::tkgetOpenFile())
if (!nchar(path)){
tcltk::tkmessageBox(message="* No file was selected! \n* Exit. \n\n\n* I love you! \n\n\n* You are not alone,... \n perhaps,...\n piggy is with you \n or doggy and doggy are with you,... ")
return(message("Suspend a process."))
}else{
xlsxfilename <- basename(path)
tcltk::tkmessageBox(message=paste("* The selected file name:",xlsxfilename,".
* More precisely, its path is the follwing:\n", path, "."))
}
# path
xlsxfilename <- basename(path)
directoryname <- dirname(path)
########### PASS ########## Fin
message("\n \n \n \n Wait...")
fileTP<-xlsx::read.xlsx(path, sheetIndex ="TP")
message("\n Wait...")
fileFP<-xlsx::read.xlsx(path, sheetIndex ="FP")
message("\n Wait... \n\n\n")
fileTruth<-xlsx::read.xlsx(path, sheetIndex ="Truth")
# head(fileTP )
# head(fileFP )
# head(fileTruth)
# names(fileTP)
# names(fileFP )
# names(fileTruth)
#
# C <- 5
# M<-5
# Q<-4
# M <-max(max(fileTP$ModalityID),max( fileFP$ModalityID))
# M <-as.integer (max( max( fileFP$ModalityID)))
# Q <-as.integer (max( max( fileTP$ReaderID )))
SRSC <-array(0,list(M,Q,C))
hhh <-array(0,list(M,Q,C))
fff <-array(0,list(M,Q,C))
for (iii in 1:nrow(fileTP)) {
hhh[ fileTP$ModalityID[iii] , fileTP$ReaderID[iii] ,fileTP$TP_Rating[iii] ] <- 1+ hhh[ fileTP$ModalityID[iii] , fileTP$ReaderID[iii] ,fileTP$TP_Rating[iii] ]
}
for (iii in 1:nrow(fileFP)) {
fff[ fileFP$ModalityID[iii] , fileFP$ReaderID[iii] ,fileFP$FP_Rating[iii] ] <- 1+ fff[ fileFP$ModalityID[iii] , fileFP$ReaderID[iii] ,fileFP$FP_Rating[iii] ]
}
######################################################
m<- rep(1:M, each=C*Q)
m
q <- rep(seq(1,Q,1), M, each=C)
q
c <-rep(rep(C:1), M*Q)
c
f <-array(0,list(M*Q*C))
f
h <-array(0,list(M*Q*C))
h
#
# length(m)
# length(q)
# length(c)
# length(h)
# length(f)
for (mmm in 1:M) {
for (qqq in 1:Q) {
for (ccc in 1:C) {
h[(mmm-1)*Q*C+(qqq-1)*C+(C+1-ccc)] <-hhh[mmm,qqq,ccc]
f[(mmm-1)*Q*C+(qqq-1)*C+(C+1-ccc)] <-fff[mmm,qqq,ccc]
}}}
# length(m)
# length(q)
# length(c)
# length(h)
# length(f)
yourdata <- data.frame(ModalityID=m,
ReaderID=q,
Confidence_lebels=c,
True_Positives=h,
False_Positives=f)
# yourdata
# nrow(yourdata)
# nrow(yourdata)==M*Q*C
message("\n-------------------------------------------------- \n\n")
message(paste("* An excel file named converted_",xlsxfilename," is made in the directory (",directoryname,"), in which the number of false positives and true positives are calcultated for your data (",xlsxfilename ,"). \n \n ",sep = ""))
xlsx::write.xlsx(yourdata, file=paste(directoryname,"/converted_",xlsxfilename,sep=""), sheetName="TPsFPs", row.names=F)
tcltk::tkmessageBox(
message=paste("
* An excel file named
converted_",xlsxfilename,"
is made in the directory
",directoryname,"
in which the number of false positives and true positives are calcultated for the
",xlsxfilename ,". \n \n ",sep = ""))
#xlsx::write.xlsx(yourdata, file=paste(directoryname,"/trans... This / is important!! This is it in path !!
# data.transformed <<- read.xlsx2(paste("transformed",xlsxfilename), 1, stringsAsFactors=FALSE)
# message("2) A dataframe is made and named 'dataframe.transformed'. \n")
# message("-------------------------------------------------- \n")
NL <- nrow(fileTruth)- nrow(subset(fileTruth ,fileTruth$LesionID ==0) )
dataList <- list(m=m,q=q,c=c,h=h,f=f, NL=NL, C=C,M=M,Q=Q)
# assign("dataList", dataList, envir=globalenv())
message("* I love you 2019. Aug. 1. so,... please please me! \n\n")
message("* The retrun value is a list object which is available for model fitting by the fit_Bayesian_FROC(). \n\n")
message("* If forget to restore the return value, then use the R object [ .Last.value ] in which the disired return value is retained. \n\n")
message("* For example, execute the R script [ fit <- fit_Bayesian_FROC(.Last.value ) ] in which the disired analysis will be done. \n\n")
message("-------------------------------------------------- \n")
return(dataList)
#
# dataList.Chakra.Web$m==dataList.transformed$m
# dataList.Chakra.Web$q==dataList.transformed$q
# dataList.Chakra.Web$c==dataList.transformed$c
# dataList.Chakra.Web$h==dataList.transformed$h
# dataList.Chakra.Web$f==dataList.transformed$f
# dataList.Chakra.Web$NL==dataList.transformed$NL
# dataList.Chakra.Web$C==dataList.transformed$C
# dataList.Chakra.Web$M==dataList.transformed$M
# dataList.Chakra.Web$Q==dataList.transformed$Q
#
# m <- data.transformed$ModalityID
# q <- data.transformed$ReaderID
# c <- data.transformed$Confidence_lebels
# h <- data.transformed$True_Positives
# f <- data.transformed$False_Positives
#
#
# length(m)
# length(q)
# length(c)
# length(h)
# length(f)
# N <-M*Q*C
#
#
# m <-data$m
# q <-data$q
# c <-data$c
# h <-data$h
# f <-data$f
# NL<-data$NL
# C<-data$C
# M<-data$M
# Q<-data$Q
#
}
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